LSM Database

LSM DB is a Log-Structured Merge database written in C++

  • Since:2019

#What is LSM?

LSM (Log-Structured Merge-Tree) is a type of database that is specifically designed for write-intensive workloads. It is a disk-based storage engine, which means it is optimized for systems that require fast data writes and can handle large volumes of data. LSM databases are widely used in many applications, including search engines, analytics, and time-series databases.

#LSM Key Features

Here are some of the most recognizable features of LSM databases:

  • Designed for write-intensive workloads: LSM databases are optimized for systems that require fast data writes and can handle large volumes of data.
  • High write throughput: LSM databases have high write throughput and can efficiently handle large volumes of writes.
  • Supports flexible indexing: LSM databases support flexible indexing schemes, including full-text search and geospatial indexing.
  • Supports transactions: LSM databases support transactions, which means they ensure data consistency and atomicity.
  • Supports efficient data compaction: LSM databases use a data compaction process to ensure efficient use of disk space and to minimize fragmentation.
  • High scalability: LSM databases can scale horizontally, which means they can handle large volumes of data across multiple nodes.

#LSM Use-Cases

Some use cases of LSM databases include:

  • Analytics: LSM databases are well-suited for large-scale analytics workloads that require high write throughput and support flexible indexing schemes.
  • Time-series data: LSM databases are commonly used for time-series databases that require efficient handling of large volumes of data and support for high write throughput.
  • Search engines: LSM databases are used in search engines because they support flexible indexing schemes, including full-text search.

#LSM Summary

LSM databases are designed for write-intensive workloads, support efficient data compaction and flexible indexing, and are commonly used in applications such as analytics, time-series databases, and search engines.

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